Analitica Descriptiva

Analitica Descriptiva#

Importacion de paquetes#

import numpy as np
import pandas as pd
import plotly.express as px
from sklearn.tree import DecisionTreeClassifier
import os
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
import plotly.graph_objs as go
import statsmodels.api as sm
from statsmodels.formula.api import ols
from statsmodels.formula.api import logit
import statsmodels.api as sm
from scipy import stats
import psycopg2

Carga de datos#

Empezaremos el proceso de exploratorio de los datos con la carga de los mismos:

conn = psycopg2.connect(
    dbname="neondb",
    user="neondb_owner",
    password="YexCUXob4Oy7",
    host="ep-little-feather-a5rorog4.us-east-2.aws.neon.tech",
    port="5432"
)
cursor = conn.cursor()

cursor.execute("SELECT * FROM companies")

# Obtener los datos y guardarlos en un DataFrame
data2 = cursor.fetchall()
data = pd.DataFrame(data2)

data.columns=['company', 'sector', 'horizon_days', 'amount', 'date_BUY_fix',
       'date_SELL_fix', 'price_BUY', 'price_SELL', 'Volatility_Buy',
       'Volatility_sell', 'Sharpe_Ratio', 'expected_return_yearly',
       'inflation', 'nominal_return', 'investment', 'ESG_ranking', 'PE_ratio',
       'EPS_ratio', 'PS_ratio', 'PB_ratio', 'NetProfitMargin_ratio',
       'current_ratio', 'roa_ratio', 'roe_ratio']

Visualizaremos los datos:

data=pd.DataFrame(data)
data.head()

	company	sector	horizon_days	amount	date_BUY_fix	date_SELL_fix	price_BUY	price_SELL	Volatility_Buy	Volatility_sell	...	investment	ESG_ranking	PE_ratio	EPS_ratio	PS_ratio	PB_ratio	NetProfitMargin_ratio	current_ratio	roa_ratio	roe_ratio
0	HMC	AUTO	7	500.0	2016-06-14	2016-06-21	25.600000	26.410000	0.274761	0.274900	...	GOOD	28.5	13.50	1.64	0.32	0.00	2.40	1.11	1.93	4.87
1	MSFT	TECH	330	1500.0	2016-07-22	2017-06-16	52.356224	66.406387	0.279043	0.160044	...	GOOD	14.7	23.18	2.05	4.19	5.15	22.53	2.35	11.28	27.29
2	BAC	BANK	15	25000.0	2015-01-28	2015-02-12	13.609470	14.925651	0.228531	0.232711	...	GOOD	26.3	42.16	0.38	1.77	0.69	4.62	0.94	0.21	1.88
3	KSS	RETAIL	270	50000.0	2015-01-28	2015-10-26	47.355690	36.093388	0.221473	0.292144	...	BAD	12.9	10.87	4.26	0.50	1.55	4.56	1.95	6.03	14.78
4	AAPL	TECH	600	10000.0	2015-05-21	2017-01-10	30.166021	28.201811	0.217025	0.227415	...	BAD	16.5	14.06	2.02	3.19	5.08	22.53	1.16	19.56	39.44

5 rows × 24 columns

Por medio de las funciones describe() y info() obtendremos una visión preliminar del tipo de datos que tenemos del DataFrame.

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 900 entries, 0 to 899
Data columns (total 24 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 company                 900 non-null    object 
 sector                  900 non-null    object 
 horizon_days            900 non-null    int64  
 amount                  900 non-null    float64
 date_BUY_fix            900 non-null    object 
 date_SELL_fix           900 non-null    object 
 price_BUY               900 non-null    float64
 price_SELL              900 non-null    float64
 Volatility_Buy          900 non-null    float64
 Volatility_sell         900 non-null    float64
Sharpe_Ratio            900 non-null    float64
expected_return_yearly  900 non-null    float64
inflation               900 non-null    float64
nominal_return          900 non-null    float64
investment              900 non-null    object 
ESG_ranking             900 non-null    float64
PE_ratio                900 non-null    float64
EPS_ratio               900 non-null    float64
PS_ratio                900 non-null    float64
PB_ratio                900 non-null    float64
NetProfitMargin_ratio   900 non-null    float64
current_ratio           900 non-null    float64
roa_ratio               900 non-null    float64
roe_ratio               900 non-null    float64
dtypes: float64(18), int64(1), object(5)
memory usage: 168.9+ KB

Manipulación de la data#

Podemos observar como la variable “Unname: 0”, “company”, “date_BUY_fix” y “date_SELL_fix” no aportan significado al proceso predictivo al brindar información muy indiviual de cada dato. Por lo que, los eliminamos.

clean = ["company", "date_BUY_fix", "date_SELL_fix"]
data_1 = data.drop(clean, axis =1)
data_1.head()

	sector	horizon_days	amount	price_BUY	price_SELL	Volatility_Buy	Volatility_sell	Sharpe_Ratio	expected_return_yearly	inflation	...	investment	ESG_ranking	PE_ratio	EPS_ratio	PS_ratio	PB_ratio	NetProfitMargin_ratio	current_ratio	roa_ratio	roe_ratio
0	AUTO	7	500.0	25.600000	26.410000	0.274761	0.274900	0.274761	-0.000176	-0.2	...	GOOD	28.5	13.50	1.64	0.32	0.00	2.40	1.11	1.93	4.87
1	TECH	330	1500.0	52.356224	66.406387	0.279043	0.160044	0.279043	0.197311	-0.2	...	GOOD	14.7	23.18	2.05	4.19	5.15	22.53	2.35	11.28	27.29
2	BANK	15	25000.0	13.609470	14.925651	0.228531	0.232711	0.228531	0.007771	-0.5	...	GOOD	26.3	42.16	0.38	1.77	0.69	4.62	0.94	0.21	1.88
3	RETAIL	270	50000.0	47.355690	36.093388	0.221473	0.292144	0.221473	0.005071	-0.5	...	BAD	12.9	10.87	4.26	0.50	1.55	4.56	1.95	6.03	14.78
4	TECH	600	10000.0	30.166021	28.201811	0.217025	0.227415	0.217025	0.403993	-0.5	...	BAD	16.5	14.06	2.02	3.19	5.08	22.53	1.16	19.56	39.44

5 rows × 21 columns

Pie Plot#

conteo_categorias = data_1['investment'].value_counts()

# Crear un DataFrame con los datos del conteo de categorías
df_conteo = pd.DataFrame({'investment': conteo_categorias.index, 'investment': conteo_categorias.values})

# Crear el gráfico de pastel con Plotly
fig = px.pie(df_conteo, values='investment', names='investment', 
             title='Distribución de la Variable investment')

# Mostrar el gráfico
fig.show()

Histograma#

fig = px.histogram(data_1, x='investment', color='investment', 
                   labels={'investment': 'invesment', 'count': 'Frecuencia'},
                   title='Distribución de la Variable Investment')

# Actualizar diseño del gráfico
fig.update_layout(xaxis_title='Variable Categórica',
                  yaxis_title='Frecuencia')

# Mostrar el gráfico
fig.show()

# Filtrar datos para deslizamientos igual a 1 y 0
df_desl_1 = data_1[data_1['investment'] == 'GOOD']
df_desl_0 = data_1[data_1['investment'] == 'BAD']

# Crear histograma para deslizamientos igual a 1
histogram_1 = go.Histogram(x=df_desl_1["inflation"], opacity=0.5, name='investment = GOOD')

# Crear histograma para deslizamientos igual a 0
histogram_0 = go.Histogram(x=df_desl_0["inflation"], opacity=0.5, name='investment = BAD')

# Configurar el diseño del gráfico
layout = go.Layout(title='Histograma de inflation',
                   xaxis=dict(title='inflation'),
                   yaxis=dict(title='Frecuencia'),
                   barmode='overlay')

# Crear la figura y agregar los histogramas
fig = go.Figure(data=[histogram_1, histogram_0], layout=layout)

# Mostrar el gráfico
fig.show()

# Filtrar datos para deslizamientos igual a 1 y 0
df_desl_1 = data_1[data_1['investment'] == 'GOOD']
df_desl_0 = data_1[data_1['investment'] == 'BAD']

# Crear histograma para deslizamientos igual a 1
histogram_1 = go.Histogram(x=df_desl_1["nominal_return"], opacity=0.5, name='investment = GOOD')

# Crear histograma para deslizamientos igual a 0
histogram_0 = go.Histogram(x=df_desl_0["nominal_return"], opacity=0.5, name='investment = BAD')

# Configurar el diseño del gráfico
layout = go.Layout(title='Histograma de nominal_return',
                   xaxis=dict(title='inflation'),
                   yaxis=dict(title='Frecuencia'),
                   barmode='overlay')

# Crear la figura y agregar los histogramas
fig = go.Figure(data=[histogram_1, histogram_0], layout=layout)

# Mostrar el gráfico
fig.show()

# Filtrar datos para deslizamientos igual a 1 y 0
df_desl_1 = data_1[data_1['investment'] == 'GOOD']
df_desl_0 = data_1[data_1['investment'] == 'BAD']

# Crear histograma para deslizamientos igual a 1
histogram_1 = go.Histogram(x=df_desl_1["amount"], opacity=0.5, name='investment = GOOD')

# Crear histograma para deslizamientos igual a 0
histogram_0 = go.Histogram(x=df_desl_0["amount"], opacity=0.5, name='investment = BAD')

# Configurar el diseño del gráfico
layout = go.Layout(title='Histograma de nominal_return',
                   xaxis=dict(title='amount'),
                   yaxis=dict(title='Frecuencia'),
                   barmode='overlay')

# Crear la figura y agregar los histogramas
fig = go.Figure(data=[histogram_1, histogram_0], layout=layout)

# Mostrar el gráfico
fig.show()

# Filtrar datos para deslizamientos igual a 1 y 0
df_desl_1 = data_1[data_1['investment'] == 'GOOD']
df_desl_0 = data_1[data_1['investment'] == 'BAD']

# Crear histograma para deslizamientos igual a 1
histogram_1 = go.Histogram(x=df_desl_1["Sharpe_Ratio"], opacity=0.5, name='investment = GOOD')

# Crear histograma para deslizamientos igual a 0
histogram_0 = go.Histogram(x=df_desl_0["Sharpe_Ratio"], opacity=0.5, name='investment = BAD')

# Configurar el diseño del gráfico
layout = go.Layout(title='Histograma de Sharpe_Ratio',
                   xaxis=dict(title='inflSharpe_Ratioation'),
                   yaxis=dict(title='Frecuencia'),
                   barmode='overlay')

# Crear la figura y agregar los histogramas
fig = go.Figure(data=[histogram_1, histogram_0], layout=layout)

# Mostrar el gráfico
fig.show()

# Filtrar datos para deslizamientos igual a 1 y 0
df_desl_1 = data_1[data_1['investment'] == 'GOOD']
df_desl_0 = data_1[data_1['investment'] == 'BAD']

# Crear histograma para deslizamientos igual a 1
histogram_1 = go.Histogram(x=df_desl_1["roa_ratio"], opacity=0.5, name='investment = GOOD')

# Crear histograma para deslizamientos igual a 0
histogram_0 = go.Histogram(x=df_desl_0["roa_ratio"], opacity=0.5, name='investment = BAD')

# Configurar el diseño del gráfico
layout = go.Layout(title='Histograma de roa_ratio',
                   xaxis=dict(title='roa_ratio'),
                   yaxis=dict(title='Frecuencia'),
                   barmode='overlay')

# Crear la figura y agregar los histogramas
fig = go.Figure(data=[histogram_1, histogram_0], layout=layout)

# Mostrar el gráfico
fig.show()

Podemos evidenciar como las variables se distribuyen y como esto se relaciona con la categoría investment.

Diagrama de dispersióin#

# Crear el gráfico de dispersión con Plotly
fig = px.scatter(data_1, x='inflation', y='nominal_return', color= "investment",
                 title='Diagrama de Dispersión',
                 labels={'inflation': 'inflation', 'nominal_return': 'nominal_return'})

# Mostrar el gráfico
fig.show()

# Crear el gráfico de dispersión con Plotly
fig = px.scatter(data_1, x='inflation', y='amount', color= "investment",
                 title='Diagrama de Dispersión',
                 labels={'inflation': 'inflation', 'amount': 'amount'})

# Mostrar el gráfico
fig.show()

# Crear el gráfico de dispersión con Plotly
fig = px.scatter(data_1, x='inflation', y='Sharpe_Ratio', color= "investment",
                 title='Diagrama de Dispersión',
                 labels={'inflation': 'inflation', 'Sharpe_Ratio': 'Sharpe_Ratio'})

# Mostrar el gráfico
fig.show()

# Crear el gráfico de dispersión con Plotly
fig = px.scatter(data_1, x='inflation', y='expected_return_yearly', color= "investment",
                 title='Diagrama de Dispersión',
                 labels={'inflation': 'inflation', 'expected_return_yearly': 'expected_return_yearly'})

# Mostrar el gráfico
fig.show()

fig = px.scatter_3d(data_1, x='inflation', y='nominal_return', z='expected_return_yearly', color='investment',
                    title='Gráfico de Dispersión',
                    labels={'inflation': 'inflation', 'nominal_return': 'nominal_return', 'expected_return_yearly': 'expected_return_yearly', 'investment': 'investment'})

# Mostrar el gráfico
fig.show()

Comparando con variable Respuesta#

Podemos observar como las variables se comportan según la categoría de investment que presenten. siendo así como “inflation” y “nominal_ return” cuentan con el comportamiento más particular.

for column in data_1.columns:
    fig = px.box(data_1, x= "investment", y=column, color = "investment")
    fig.show()

Es posible observar como se evidenciaba en el arbol de clasificación que los variables con mayor incidencia eran Inflation y nominal return.

Sector, horizon day, amount, price sell, price buy, volatility buy, volatility sell, sharpe ratio, expect return, ESG ranking, PE ratio, EPS ratio, PS ratio, NetProfitmargin, current ratio, roa ratio y roe ratio no presentan aparentemente una fuerte tendencia hacia una categoria. Es decir, visualmente, no parecen ser determinantes en clasificar a una inversión como GOOD o BAD.

fig = px.histogram(data, x='sector', color='investment')
fig.show()

Podemos visualizar que no hay presencia de un sector que presente una predominante tendencia de buena inversión o mala inversión. Y al ser la unica variable categorica, se decide no tenerla en consideración en el entrenamiento del modelo.

data_1= data_1.drop("sector", axis=1)

Correlaciones#

Usar correlación para verificar que datos se las variables independientes no cuenten con una relación lineal.

numeric_df = data_1.select_dtypes(include=['int', 'float'])


# Calcular matriz de correlación
matriz_correlacion = numeric_df.corr()
matriz_correlacion

	horizon_days	amount	price_BUY	price_SELL	Volatility_Buy	Volatility_sell	Sharpe_Ratio	expected_return_yearly	inflation	nominal_return	ESG_ranking	PE_ratio	EPS_ratio	PS_ratio	PB_ratio	NetProfitMargin_ratio	current_ratio	roa_ratio	roe_ratio
horizon_days	1.000000	0.057634	-0.019103	0.041087	-0.000782	0.079032	-0.000782	0.557271	0.000982	0.314202	-0.056352	-0.030855	0.043956	0.058744	0.000010	0.049919	0.063964	0.059054	0.045002
amount	0.057634	1.000000	0.015068	0.019191	0.036155	-0.017119	0.036155	0.022659	0.024025	0.035272	0.041535	-0.025461	-0.005007	0.003251	0.026403	-0.072575	-0.033395	-0.089766	-0.098832
price_BUY	-0.019103	0.015068	1.000000	0.975333	-0.074075	-0.054223	-0.074075	0.105182	0.091847	0.046071	0.060159	0.317998	0.679935	0.219801	0.367833	0.112021	0.161695	0.158972	0.004417
price_SELL	0.041087	0.019191	0.975333	1.000000	-0.071000	-0.062839	-0.071000	0.165045	0.083859	0.142205	0.058317	0.304729	0.698000	0.222112	0.350046	0.122658	0.172211	0.161950	0.002011
Volatility_Buy	-0.000782	0.036155	-0.074075	-0.071000	1.000000	0.850632	1.000000	0.166545	0.050158	0.065940	-0.079210	0.033027	-0.225247	0.237593	0.230903	-0.156604	0.194057	-0.054318	-0.265809
Volatility_sell	0.079032	-0.017119	-0.054223	-0.062839	0.850632	1.000000	0.850632	0.171310	0.070344	-0.039453	-0.123275	0.035551	-0.211890	0.187969	0.176131	-0.175065	0.175390	-0.040506	-0.258205
Sharpe_Ratio	-0.000782	0.036155	-0.074075	-0.071000	1.000000	0.850632	1.000000	0.166545	0.050158	0.065940	-0.079210	0.033027	-0.225247	0.237593	0.230903	-0.156604	0.194057	-0.054318	-0.265809
expected_return_yearly	0.557271	0.022659	0.105182	0.165045	0.166545	0.171310	0.166545	1.000000	0.022885	0.533747	-0.088662	0.085136	0.038070	0.391668	0.361000	0.199539	0.263973	0.243156	-0.021053
inflation	0.000982	0.024025	0.091847	0.083859	0.050158	0.070344	0.050158	0.022885	1.000000	0.003025	-0.004853	-0.007222	-0.008672	0.048510	0.116725	-0.034308	-0.009438	0.041586	0.038014
nominal_return	0.314202	0.035272	0.046071	0.142205	0.065940	-0.039453	0.065940	0.533747	0.003025	1.000000	-0.105205	0.025856	0.072892	0.118291	0.038219	0.152078	0.101896	0.134162	0.049182
ESG_ranking	-0.056352	0.041535	0.060159	0.058317	-0.079210	-0.123275	-0.079210	-0.088662	-0.004853	-0.105205	1.000000	0.112499	-0.106382	0.164561	0.012402	-0.138176	0.118752	-0.429422	-0.469440
PE_ratio	-0.030855	-0.025461	0.317998	0.304729	0.033027	0.035551	0.033027	0.085136	-0.007222	0.025856	0.112499	1.000000	-0.042664	0.117468	0.352464	-0.036344	0.054029	-0.029921	-0.064672
EPS_ratio	0.043956	-0.005007	0.679935	0.698000	-0.225247	-0.211890	-0.225247	0.038070	-0.008672	0.072892	-0.106382	-0.042664	1.000000	0.078800	-0.045096	0.329119	0.130749	0.311497	0.243385
PS_ratio	0.058744	0.003251	0.219801	0.222112	0.237593	0.187969	0.237593	0.391668	0.048510	0.118291	0.164561	0.117468	0.078800	1.000000	0.483868	0.522420	0.822391	0.446526	-0.029877
PB_ratio	0.000010	0.026403	0.367833	0.350046	0.230903	0.176131	0.230903	0.361000	0.116725	0.038219	0.012402	0.352464	-0.045096	0.483868	1.000000	-0.043134	0.182139	0.117530	-0.176910
NetProfitMargin_ratio	0.049919	-0.072575	0.112021	0.122658	-0.156604	-0.175065	-0.156604	0.199539	-0.034308	0.152078	-0.138176	-0.036344	0.329119	0.522420	-0.043134	1.000000	0.474356	0.733180	0.560262
current_ratio	0.063964	-0.033395	0.161695	0.172211	0.194057	0.175390	0.194057	0.263973	-0.009438	0.101896	0.118752	0.054029	0.130749	0.822391	0.182139	0.474356	1.000000	0.452459	0.111891
roa_ratio	0.059054	-0.089766	0.158972	0.161950	-0.054318	-0.040506	-0.054318	0.243156	0.041586	0.134162	-0.429422	-0.029921	0.311497	0.446526	0.117530	0.733180	0.452459	1.000000	0.710006
roe_ratio	0.045002	-0.098832	0.004417	0.002011	-0.265809	-0.258205	-0.265809	-0.021053	0.038014	0.049182	-0.469440	-0.064672	0.243385	-0.029877	-0.176910	0.560262	0.111891	0.710006	1.000000

heatmap = go.Heatmap(
    z=matriz_correlacion.values,  # valores de la correlación
    x=matriz_correlacion.columns,  # nombres de las columnas
    y=matriz_correlacion.columns,  # nombres de las filas
    colorscale='Viridis',  # paleta de colores
)

layout = go.Layout(title='Mapa de calor de correlación')

fig = go.Figure(data=[heatmap], layout=layout)

# Mostrar el mapa de calor
#pyo.plot(fig, filename='heatmap.html')
fig.show()

Exploración: Modelo Reg. Logistica#

Como hemos visto anteriormente, la mayor parte de las variables no parecen aportar a conocer la variable respuesta. Por lo que, en modo de exploración realizamos un modelo de regresion de logística para conocer un poco más los datos con los que tratamos.

modelo_logit = sm.Logit(data_1['investment'], data_1[['Volatility_sell', 'expected_return_yearly', 'inflation', 'nominal_return', 'price_SELL','price_BUY']])
resultado_modelo = modelo_logit.fit()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[24], line 1
----> 1 modelo_logit = sm.Logit(data_1['investment'], data_1[['Volatility_sell', 'expected_return_yearly', 'inflation', 'nominal_return', 'price_SELL','price_BUY']])
      2 resultado_modelo = modelo_logit.fit()

File ~\anaconda3\envs\env_pf_data_viz\lib\site-packages\statsmodels\discrete\discrete_model.py:475, in BinaryModel.__init__(self, endog, exog, offset, check_rank, **kwargs)
    472 def __init__(self, endog, exog, offset=None, check_rank=True, **kwargs):
    473     # unconditional check, requires no extra kwargs added by subclasses
    474     self._check_kwargs(kwargs)
--> 475     super().__init__(endog, exog, offset=offset, check_rank=check_rank,
    476                      **kwargs)
    477     if not issubclass(self.__class__, MultinomialModel):
    478         if not np.all((self.endog >= 0) & (self.endog <= 1)):

File ~\anaconda3\envs\env_pf_data_viz\lib\site-packages\statsmodels\discrete\discrete_model.py:185, in DiscreteModel.__init__(self, endog, exog, check_rank, **kwargs)
    183 def __init__(self, endog, exog, check_rank=True, **kwargs):
    184     self._check_rank = check_rank
--> 185     super().__init__(endog, exog, **kwargs)
    186     self.raise_on_perfect_prediction = False  # keep for backwards compat
    187     self.k_extra = 0

File ~\anaconda3\envs\env_pf_data_viz\lib\site-packages\statsmodels\base\model.py:270, in LikelihoodModel.__init__(self, endog, exog, **kwargs)
    269 def __init__(self, endog, exog=None, **kwargs):
--> 270     super().__init__(endog, exog, **kwargs)
    271     self.initialize()

File ~\anaconda3\envs\env_pf_data_viz\lib\site-packages\statsmodels\base\model.py:95, in Model.__init__(self, endog, exog, **kwargs)
     93 missing = kwargs.pop('missing', 'none')
     94 hasconst = kwargs.pop('hasconst', None)
---> 95 self.data = self._handle_data(endog, exog, missing, hasconst,
     96                               **kwargs)
     97 self.k_constant = self.data.k_constant
     98 self.exog = self.data.exog

File ~\anaconda3\envs\env_pf_data_viz\lib\site-packages\statsmodels\base\model.py:135, in Model._handle_data(self, endog, exog, missing, hasconst, **kwargs)
    134 def _handle_data(self, endog, exog, missing, hasconst, **kwargs):
--> 135     data = handle_data(endog, exog, missing, hasconst, **kwargs)
    136     # kwargs arrays could have changed, easier to just attach here
    137     for key in kwargs:

File ~\anaconda3\envs\env_pf_data_viz\lib\site-packages\statsmodels\base\data.py:675, in handle_data(endog, exog, missing, hasconst, **kwargs)
    672     exog = np.asarray(exog)
    674 klass = handle_data_class_factory(endog, exog)
--> 675 return klass(endog, exog=exog, missing=missing, hasconst=hasconst,
    676              **kwargs)

File ~\anaconda3\envs\env_pf_data_viz\lib\site-packages\statsmodels\base\data.py:84, in ModelData.__init__(self, endog, exog, missing, hasconst, **kwargs)
     82     self.orig_endog = endog
     83     self.orig_exog = exog
---> 84     self.endog, self.exog = self._convert_endog_exog(endog, exog)
     86 self.const_idx = None
     87 self.k_constant = 0

File ~\anaconda3\envs\env_pf_data_viz\lib\site-packages\statsmodels\base\data.py:509, in PandasData._convert_endog_exog(self, endog, exog)
    507 exog = exog if exog is None else np.asarray(exog)
    508 if endog.dtype == object or exog is not None and exog.dtype == object:
--> 509     raise ValueError("Pandas data cast to numpy dtype of object. "
    510                      "Check input data with np.asarray(data).")
    511 return super()._convert_endog_exog(endog, exog)

ValueError: Pandas data cast to numpy dtype of object. Check input data with np.asarray(data).

p_valores = resultado_modelo.pvalues
variables_significativas = p_valores[p_valores < 0.05]
variables_significativas

Volatility_sell           2.623637e-13
expected_return_yearly    1.624640e-02
inflation                 1.012144e-22
nominal_return            8.670997e-23
price_SELL                1.149075e-04
price_BUY                 1.378763e-04
dtype: float64

resultado_modelo.summary()

Logit Regression Results
Dep. Variable:	investment	No. Observations:	900
Model:	Logit	Df Residuals:	894
Method:	MLE	Df Model:	5
Date:	Sun, 21 Apr 2024	Pseudo R-squ.:	0.6825
Time:	16:12:04	Log-Likelihood:	-190.05
converged:	True	LL-Null:	-598.63
Covariance Type:	nonrobust	LLR p-value:	2.246e-174

	coef	std err	z	P>\|z\|	[0.025	0.975]
Volatility_sell	-6.5713	0.899	-7.312	0.000	-8.333	-4.810
expected_return_yearly	-4.4793	1.864	-2.403	0.016	-8.132	-0.826
inflation	-7.1526	0.729	-9.811	0.000	-8.581	-5.724
nominal_return	24.9465	2.539	9.826	0.000	19.971	29.922
price_SELL	-0.0258	0.007	-3.857	0.000	-0.039	-0.013
price_BUY	0.0279	0.007	3.812	0.000	0.014	0.042

Possibly complete quasi-separation: A fraction 0.38 of observations can be
perfectly predicted. This might indicate that there is complete
quasi-separation. In this case some parameters will not be identified.

Con el método anteriormente utilizado de exploración podemos confirmar nuevamente que: “inflation” y “nominal return” son las que mayor capacidad de aporte tienen. Seguido de Volatility_sell, expected_return_yearly, nominal_return e inflation.

Como conclusión del proceso exploratorio de datos, podríamos mencionar que la mayor parte de las variables presentes en el data frame no tienen una fuerte inclinación a hacia la variable respuesta categorica asociada. Como fue posible revisar durante los box plots y durante el arbol de regresión expuesto para el estudio de las variables, solamente algunas variables parecen ser determinantes para conocer si una inversión será buena o mala. Adicionalmente, por medio de un modelo de regresion hecho en modo didactivo y exploratorio, fue posible observa que la mayor parte de las variables no son significativas a nivel predictivo.